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--- |
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license: cc-by-4.0 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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base_model: l3cube-pune/hing-roberta |
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model-index: |
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- name: hing-roberta-NCM-run-1 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# hing-roberta-NCM-run-1 |
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This model is a fine-tuned version of [l3cube-pune/hing-roberta](https://huggingface.co/l3cube-pune/hing-roberta) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.2912 |
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- Accuracy: 0.6667 |
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- Precision: 0.6513 |
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- Recall: 0.6494 |
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- F1: 0.6502 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 20 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 0.8968 | 1.0 | 927 | 0.8552 | 0.6257 | 0.6508 | 0.5961 | 0.5969 | |
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| 0.7022 | 2.0 | 1854 | 1.1142 | 0.3937 | 0.3270 | 0.3273 | 0.2051 | |
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| 0.5569 | 3.0 | 2781 | 0.9130 | 0.6591 | 0.6566 | 0.6612 | 0.6509 | |
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| 0.363 | 4.0 | 3708 | 1.6630 | 0.6526 | 0.6634 | 0.6414 | 0.6436 | |
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| 0.2801 | 5.0 | 4635 | 2.0458 | 0.6451 | 0.6339 | 0.6345 | 0.6330 | |
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| 0.1925 | 6.0 | 5562 | 2.3378 | 0.6570 | 0.6439 | 0.6254 | 0.6277 | |
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| 0.1297 | 7.0 | 6489 | 2.5205 | 0.6839 | 0.6719 | 0.6651 | 0.6675 | |
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| 0.114 | 8.0 | 7416 | 2.8373 | 0.6505 | 0.6379 | 0.6249 | 0.6280 | |
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| 0.0994 | 9.0 | 8343 | 2.5358 | 0.6634 | 0.6539 | 0.6446 | 0.6474 | |
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| 0.0977 | 10.0 | 9270 | 2.8244 | 0.6537 | 0.6489 | 0.6210 | 0.6238 | |
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| 0.0623 | 11.0 | 10197 | 2.7593 | 0.6764 | 0.6602 | 0.6487 | 0.6510 | |
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| 0.0537 | 12.0 | 11124 | 2.9823 | 0.6677 | 0.6679 | 0.6450 | 0.6488 | |
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| 0.0432 | 13.0 | 12051 | 3.0792 | 0.6537 | 0.6465 | 0.6352 | 0.6378 | |
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| 0.0406 | 14.0 | 12978 | 3.0707 | 0.6688 | 0.6592 | 0.6509 | 0.6534 | |
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| 0.0296 | 15.0 | 13905 | 3.3289 | 0.6667 | 0.6596 | 0.6452 | 0.6486 | |
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| 0.0288 | 16.0 | 14832 | 3.2147 | 0.6645 | 0.6592 | 0.6512 | 0.6528 | |
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| 0.024 | 17.0 | 15759 | 3.3284 | 0.6645 | 0.6470 | 0.6405 | 0.6425 | |
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| 0.0201 | 18.0 | 16686 | 3.2428 | 0.6688 | 0.6515 | 0.6515 | 0.6515 | |
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| 0.0176 | 19.0 | 17613 | 3.2680 | 0.6710 | 0.6574 | 0.6536 | 0.6547 | |
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| 0.0168 | 20.0 | 18540 | 3.2912 | 0.6667 | 0.6513 | 0.6494 | 0.6502 | |
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### Framework versions |
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- Transformers 4.20.1 |
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- Pytorch 1.10.1+cu111 |
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- Datasets 2.3.2 |
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- Tokenizers 0.12.1 |
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